@inproceedings{balahur-etal-2014-resource,
title = "Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts",
author = {Balahur, Alexandra and
Turchi, Marco and
Steinberger, Ralf and
Perea-Ortega, Jose-Manuel and
Jacquet, Guillaume and
K{\"u}{\c{c}}{\"u}k, Dilek and
Zavarella, Vanni and
El Ghali, Adil},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L14-1727/",
abstract = "This paper presents an evaluation of the use of machine translation to obtain and employ data for training multilingual sentiment classifiers. We show that the use of machine translated data obtained similar results as the use of native-speaker translations of the same data. Additionally, our evaluations pinpoint to the fact that the use of multilingual data, including that obtained through machine translation, leads to improved results in sentiment classification. Finally, we show that the performance of the sentiment classifiers built on machine translated data can be improved using original data from the target language and that even a small amount of such texts can lead to significant growth in the classification performance."
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<title>Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)</title>
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<abstract>This paper presents an evaluation of the use of machine translation to obtain and employ data for training multilingual sentiment classifiers. We show that the use of machine translated data obtained similar results as the use of native-speaker translations of the same data. Additionally, our evaluations pinpoint to the fact that the use of multilingual data, including that obtained through machine translation, leads to improved results in sentiment classification. Finally, we show that the performance of the sentiment classifiers built on machine translated data can be improved using original data from the target language and that even a small amount of such texts can lead to significant growth in the classification performance.</abstract>
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%0 Conference Proceedings
%T Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts
%A Balahur, Alexandra
%A Turchi, Marco
%A Steinberger, Ralf
%A Perea-Ortega, Jose-Manuel
%A Jacquet, Guillaume
%A Küçük, Dilek
%A Zavarella, Vanni
%A El Ghali, Adil
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F balahur-etal-2014-resource
%X This paper presents an evaluation of the use of machine translation to obtain and employ data for training multilingual sentiment classifiers. We show that the use of machine translated data obtained similar results as the use of native-speaker translations of the same data. Additionally, our evaluations pinpoint to the fact that the use of multilingual data, including that obtained through machine translation, leads to improved results in sentiment classification. Finally, we show that the performance of the sentiment classifiers built on machine translated data can be improved using original data from the target language and that even a small amount of such texts can lead to significant growth in the classification performance.
%U https://aclanthology.org/L14-1727/
Markdown (Informal)
[Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts](https://aclanthology.org/L14-1727/) (Balahur et al., LREC 2014)
ACL